1.1. Contact organisation
National Institute of Statistics, Romania
1.2. Contact organisation unit
General Directorate of Economic Statistics - Department of Agricultural and Environmental Statistics
1.3. Contact name
Confidential because of GDPR
1.4. Contact person function
Confidential because of GDPR
1.5. Contact mail address
16 Libertatii Blvd., Bucharest 5, Romania
1.6. Contact email address
Confidential because of GDPR
1.7. Contact phone number
Confidential because of GDPR
1.8. Contact fax number
Confidential because of GDPR
2.1. Metadata last certified
8 May 2025
2.2. Metadata last posted
14 May 2025
2.3. Metadata last update
8 May 2025
3.1. Data description
The data describe the structure of agricultural holdings providing the general characteristics of farms and farmers and information on their land, livestock and labour force. They also describe production methods, rural development measures and agro-environmental aspects that look at the impact of agriculture on the environment.
The data are used by public, researchers, farmers and policy-makers to better understand the state of the farming sector and the impact of agriculture on the environment. The data follow up the changes in the agricultural sector and provide a basis for decision-making in the Common Agricultural Policy (CAP) and other European Union policies.
The statistical unit is the agricultural holding (farm). The aggregated results are disseminated through statistical tables. The data are presented at different geographical levels and over periods.
The data collections are organised in line with Regulation (EU) 2018/1091 and have a new structure, consisting of a core data set and several modules. The regulation covers the data collections in 2019/2020 (the agricultural census), 2023 and 2026. The data are as comparable and coherent as possible with the other European countries.
3.2. Classification system
Data are arranged in tables using many classifications. Please find below information on most classifications.
The classifications of variables are available in Annex III of Regulation (EU) 2018/1091 and in Commission Implementing Regulation (EU) 2021/2286.
The farm typology means a uniform classification of the holdings based on their type of farming and their economic size. Both are determined on the basis of the standard gross margin (SGM) (until 2007) or standard output (SO) (from 2010 onward) which is calculated for each crop and animal. The farm type is determined by the relative contribution of the different productions to the total standard gross margin or the standard output of the holding.
The territorial classification uses the NUTS classification to break down the regional data. The regional data is available at NUTS level 2.
3.3. Coverage - sector
The statistics cover agricultural holdings undertaking agricultural activities as listed in item 3.5 below and meeting the minimum coverage requirements (thresholds) as listed in item 3.6 below.
3.4. Statistical concepts and definitions
The list of core variables is set in Annex III of Regulation (EU) 2018/1091.
The descriptions of the core variables as well as the lists and descriptions of the variables for the modules collected in 2023 are set in Commission Implementing Regulation (EU) 2021/2286.
The following groups of variables are collected in 2023:
- for core: location of the holding, legal personality of the holding, manager, type of tenure of the utilised agricultural area, variables of land, organic farming, irrigation on cultivated outdoor area, variables of livestock, organic production methods applied to animal production;
- for the module "Labour force and other gainful activities": farm management, family labour force, non-family labour force, other gainful activities directly and not directly related to the agricultural holding;
- for the module "Rural development": support received by agricultural holdings through various rural development measures;
- for the module “Irrigation”: availability of irrigation, irrigation methods, sources of irrigation water, technical parameters of the irrigation equipment, crops irrigated during a 12 months period;
- for the module “Soil management practices”: tillage methods, soil cover on arable land, crop rotation on arable land, ecological focus area;
- for the module “Machinery and equipment”: internet facilities, basic machinery, use of precision farming, machinery for livestock management, storage for agricultural products, equipment used for production of renewable energy on agricultural holdings;
- for the module “Orchards”: apples area, pears area, peaches area, nectarines area, apricots area, grapes for table use area, grapes for raisins area, each one by age of plantation and density of trees.
3.5. Statistical unit
See sub-category below.
3.5.1. Definition of agricultural holding
The agricultural holding is a single unit, both technically and economically, that has a single management and that undertakes economic activities in agriculture in accordance with Regulation (EC) No 1893/2006 belonging to groups:
- A.01.1: Growing of non-perennial crops
- A.01.2: Growing of perennial crops
- A.01.3: Plant propagation
- A.01.4: Animal production
- A.01.5: Mixed farming or
- The “maintenance of agricultural land in good agricultural and environmental condition” of group A.01.6 within the economic territory of the Union, either as its primary or secondary activity.
Regarding activities of class A.01.49, only the activities “Raising and breeding of semi-domesticated or other live animals” (with the exception of raising of insects) and “Bee-keeping and production of honey and beeswax” are included.
3.6. Statistical population
See sub-categories below.
3.6.1. Population covered by the core data sent to Eurostat (main frame and if applicable frame extension)
The thresholds of agricultural holdings are available in the annex.
Annexes:
3.6.1. Thresholds of agricultural holdings
3.6.1.1. Raised thresholds compared to Regulation (EU) 2018/1091
No3.6.1.2. Lowered and/or additional thresholds compared to Regulation (EU) 2018/1091
Yes3.6.2. Population covered by the data sent to Eurostat for the modules “Labour force and other gainful activities”, “Rural development” and “Machinery and equipment”
The same population of agricultural holdings defined in item 3.6.1.
3.6.3. Population covered by the data sent to Eurostat for the module “Animal housing and manure management”
Restricted from publication
3.6.4. Population covered by the data sent to Eurostat for the module “Irrigation”
The population of agricultural holdings defined in item 3.6.1 with total irrigable area.
3.6.5. Population covered by the data sent to Eurostat for the module “Soil management practices”
The same population of agricultural holdings defined in item 3.6.1, holdings with arable land but also holdings with elements of ecological focus areas (terraces, field margins, agroforestry, etc.) or drainage.
3.6.6. Population covered by the data sent to Eurostat for the module “Orchard”
The subset of agricultural holdings defined in item 3.6.1, with any of the individual orchard variables that meet the threshold specified in Article 7(5) of Regulation (EU) 2018/1091.
According to these thresholds, Romania transmits the data for apples, pears, peaches, nectarines, apricots and grapes for table use. However, Romania also provides data for grapes for raisins, on a voluntary basis.
3.6.7. Population covered by the data sent to Eurostat for the module “Vineyard”
Restricted from publication
3.7. Reference area
See sub-categories below.
3.7.1. Geographical area covered
The entire territory of Romania.
3.7.2. Inclusion of special territories
Not applicable.
3.7.3. Criteria used to establish the geographical location of the holding
The main building for productionThe majority of the area of the holding
The most important parcel by physical size
The most important parcel by economic size
The residence of the farmer (manager) not further than 5 km straight from the farm
3.7.4. Additional information reference area
Not available.
3.8. Coverage - Time
Farm structure statistics in Romania cover the period from 2002 onwards. Older time series are described in the previous quality reports (national methodological reports).
3.9. Base period
The 2023 data are processed (by Eurostat) with 2020 standard output coefficients (calculated as a 5-year average of the period 2018-2022). For more information, you can consult the definition of the standard output.
Two kinds of units are generally used:
- the units of measurement for the variables (area in hectares, livestock in (1000) heads or LSU (livestock units), labour force in persons or AWU (annual working units), standard output in Euro, places for animal housing etc.) and
- the number of agricultural holdings having these characteristics.
See sub-categories below.
5.1. Reference period for land variables
The use of land refers to the 12-month period from 1 October 2022 to 30 September 2023.
In the case of successive crops from the same piece of land, the land use refers to a crop that is harvested during the reference year, regardless of when the crop in question is sown.
5.2. Reference period for variables on irrigation and soil management practices
Reference period was crop year (1 October 2022 - 30 September 2023).
5.3. Reference day for variables on livestock and animal housing
The reference day 31 December 2023 for livestock variables.
The animal housing variables are not applicable for 2023.
5.4. Reference period for variables on manure management
The manure management variables are not applicable for 2023.
5.5. Reference period for variables on labour force
Reference period was crop year (1 October 2022 - 30 September 2023).
5.6. Reference period for variables on rural development measures
The three-year period ending on 31 December 2023.
5.7. Reference day for all other variables
The reference day 31 December within the reference year 2023.
6.1. Institutional Mandate - legal acts and other agreements
See sub-categories below.
6.1.1. National legal acts and other agreements
Legal act6.1.2. Name of national legal acts and other agreements
- Law No. 226 of June 5, 2009 on the organisation and functioning of official statistics in Romania, as subsequently amended and supplemented (also named Law No. 226/2009)
- Decision No. 161 of February 22, 2023 approving the Annual National Statistical Program 2023
6.1.3. Link to national legal acts and other agreements
6.1.4. Year of entry into force of national legal acts and other agreements
- Law No. 226/2009: 2009
- Decision No. 161: 2023
6.1.5. Legal obligations for respondents
Yes6.2. Institutional Mandate - data sharing
Protocols were established between NIS and relevant institutions/ministries covering agricultural statistics. The relevant institutions/ministries are Ministry of Agriculture and Rural Development, Agency for Payments and Intervention in Agriculture, and Sanitary Veterinary and Food Safety Directions.
These protocols are linked to data sharing.
7.1. Confidentiality - policy
According to Law No. 226/2009, the individual data are confidential and could be used only for statistical purposes.
Keeping the data confidentiality is mandatory for permanent and temporary staff; both categories sign a commitment to confidentiality when they are hired.
In addition, norms of statistical data confidentiality are published in the Official Journal of Romania.
The "front cover" of the electronic questionnaire includes references to the confidentiality of data and processing of personal data in accordance with GDPR. Enumerators were trained to communicate this information to farmers before each interview.
7.2. Confidentiality - data treatment
See sub-categories below.
7.2.1. Aggregated data
See sub-categories below.
7.2.1.1. Rules used to identify confidential cells
Threshold rule (The number of contributors is less than a pre-specified threshold)p% rule (A contributor is able to derive an estimate of some other contributor within p% of its true value)
7.2.1.2. Methods to protect data in confidential cells
Cell suppression (Completely suppress the value of some cells)7.2.1.3. Description of rules and methods
The aggregated data does not allow the identification of an agricultural holding through dissemination. Output dissemination is available until the county level (NUTS 3).
In some special cases, neighbouring intervals are joined to obtain larger ones, with more agricultural holdings, to protect confidentiality.
7.2.2. Microdata
See sub-categories below.
7.2.2.1. Use of EU methodology for microdata dissemination
Yes7.2.2.2. Methods of perturbation
Recoding of variablesRemoval of variables
Reduction of information
Merging categories
Rounding
Micro-aggregation
7.2.2.3. Description of methodology
Access to microdata is permitted only for scientific purposes and requires a written commitment. Access is granted under NIS's confidentiality rules, which are available at: Insse website - NIS microdata scientific purposes.
The methodology is (also) described in the dedicated section of Eurostat's website: microdata.
8.1. Release calendar
The release calendar, available on NIS's website, contains information about IFS 2023 (for press releases and publications, separately).
8.2. Release calendar access
Press releases and publications
8.3. Release policy - user access
In line with the Community legal framework and the European Statistics Code of Practice (Principle 15 - Accessibility and clarity), NIS disseminates national statistics on its website respecting professional independence and, in an objective, professional and transparent manner in which all users are treated equitably.
The NIS's official statistics calendar provides exact release dates and times, is flexible, offers topic searches, and is regularly updated.
For Integrated Farm Statistics 2023 as for all surveys, once the data are submitted to Eurostat, they are also disseminated at national level through press releases.
There is no privileged access to statistical releases in their final form, before they are made available in the public domain.
8.3.1. Use of quality rating system
Yes, the EU quality rating system8.3.1.1. Description of the quality rating system
The methodology is described in the EU handbook.
Every 10 years for censuses and every 3-4 years between census years for all other IFS/FSS data collections.
10.1. Dissemination format - News release
See sub-categories below.
10.1.1. Publication of news releases
Yes10.1.2. Link to news releases
Structural survey in agriculture
10.2. Dissemination format - Publications
See sub-categories below.
10.2.1. Production of paper publications
Yes, in English also10.2.2. Production of on-line publications
Yes, in English also10.2.3. Title, publisher, year and link
- Structural Survey in Agriculture 2023 - General data at national level - Volume 1
- Structural Survey in Agriculture 2023 - Data by macro-regions, development regions and counties - Volume 2
10.3. Dissemination format - online database
See sub-categories below.
10.3.1. Data tables - consultations
Not applicable.
10.3.2. Accessibility of online database
No10.3.3. Link to online database
Not applicable.
10.4. Dissemination format - microdata access
See sub-category below.
10.4.1. Accessibility of microdata
Yes10.5. Dissemination format - other
Not available.
10.5.1. Metadata - consultations
Not requested.
10.6. Documentation on methodology
See sub-categories below.
10.6.1. Metadata completeness - rate
Not requested.
10.6.2. Availability of national reference metadata
Yes10.6.3. Title, publisher, year and link to national reference metadata
Farm structure, National Reference Metadata in Single Integrated, Eurostat, 2024
10.6.4. Availability of national handbook on methodology
Yes10.6.5. Title, publisher, year and link to handbook
IFS 2023 - Statistical operator's handbook (it contains a detailed presentation of the IFS 2023 methodology).
IFS 2023 - Statistical operator's guide (it contains a short presentation of the IFS 2023 methodology).
Annexes:
10.6.5. Statistical operator's handbook
10.6.5. Statistical operator's guide
10.6.6. Availability of national methodological papers
No10.6.7. Title, publisher, year and link to methodological papers
Not applicable.
10.7. Quality management - documentation
11.1. Quality assurance
See sub-categories below.
11.1.1. Quality management system
Yes11.1.2. Quality assurance and assessment procedures
Training coursesQuality guidelines
Peer review
Certification
11.1.3. Description of the quality management system and procedures
NIS is guided by the provisions of Law No. 226/2009. Statistical activities are performed in accordance with the Generic Statistical Business Process Model (GSBPM), according to which the final phase of statistical activities is an overall evaluation using information gathered in each phase or sub-process.
For further details on quality assurance at NIS Romania, please see the following link: Quality national statistical system.
Information on the peer review can be found website.
11.1.4. Improvements in quality procedures
There are no ongoing or planned improvements in quality procedures.
11.2. Quality management - assessment
Not available.
12.1. Relevance - User Needs
The IFS results are used to substantiate agricultural, regional, territorial cohesion, rural development, and environment policies. Eurostat performs the consultations with EU and non-EU users of farm statistics.
At the national level, the main users of the IFS results include the Ministry of Agriculture and Rural Development, central and local public administrations, the National Academy of Agricultural and Forestry Sciences, universities and scientific researchers.
12.1.1. Main groups of variables collected only for national purposes
No variables collected for national purposes.
12.1.2. Unmet user needs
All users' needs are met.
12.1.3. Plans for satisfying unmet user needs
Not applicable.
12.2. Relevance - User Satisfaction
There is no specific procedure to measure user satisfaction for the IFS.
12.2.1. User satisfaction survey
No12.2.2. Year of user satisfaction survey
Not applicable.
12.2.3. Satisfaction level
Not applicable12.3. Completeness
Information on not collected, not-significant and not-existent variables is available on Eurostat’s website, at the website: Additional data - Eurostat (europa.eu).
12.3.1. Data completeness - rate
Not applicable for Integrated Farm Statistics as the not collected variables, not-significant variables and not-existent variables are completed with 0.
13.1. Accuracy - overall
See categories below.
13.2. Sampling error
See sub-categories below.
13.2.1. Sampling error - indicators
Please find the relative standard errors on Eurostat’s website, at the Circabc website.
13.2.2. Reasons for non-compliant precision requirements in relation to Regulation (EU) 2018/1091
For live poultry excluding cocks and chicks of chicken (A5000X5120_5130_LSU) in the NUTS 2 RO12 region, the reason for the non-compliance is that some important units have changed their location. For the other items (oilseeds (excluding cotton) (I1100XI1150T) in NUTS 2 region RO11, pears (F1120), peaches (F1210), nectarines (F1220) at country level, apples (F1110) in NUTS 2 region RO21), the reasons refer to the very small cultivated area in these regions. Regarding UAA_IB (total irrigable area) in the NUTS 1 RO1 region, the irrigable area is limited because this region is located in a mountainous area.
For the future, one of the measures that can be taken is to increase the number of observations in a sample, this can reduce statistical errors, including the relative standard error.
13.2.3. Reference on method of estimation
See in annex.
Annexes:
13.2.3 Methodology used to calculate relative standard errors
13.2.4. Impact of sampling error on data quality
Unknown13.3. Non-sampling error
See sub-categories below.
13.3.1. Coverage error
See sub-categories below.
13.3.1.1. Over-coverage - rate
The over-coverage rate is available on Eurostat’s website, at the Circabc website.
The over-coverage rate is unweighted.
The over-coverage rate is calculated as the share of ineligible holdings to the holdings designated for the core data collection. The ineligible holdings include those holdings with unknown eligibility status that are not imputed nor re-weighted for (therefore considered ineligible).
The over-coverage rate is calculated over the holdings in the main frame and frame extension, for which core data are sent to Eurostat.
13.3.1.1.1. Types of holdings included in the frame but not belonging to the population of the core (main frame and if applicable frame extension)
Below thresholds during the reference periodTemporarily out of production during the reference period
Ceased activities
Merged to another unit
13.3.1.1.2. Actions to minimize the over-coverage error
Removal of ineligible units from the records, leaving unchanged the weights for the other units13.3.1.1.3. Additional information over-coverage error
Not available.
13.3.1.2. Common units - proportion
Not requested.
13.3.1.3. Under-coverage error
See sub-categories below.
13.3.1.3.1. Under-coverage rate
Not available.
13.3.1.3.2. Types of holdings belonging to the population of the core but not included in the frame (main frame and if applicable frame extension)
New births13.3.1.3.3. Actions to minimise the under-coverage error
Use of Integrated Administration and Control System (IACS) data to detect new units.
13.3.1.3.4. Additional information under-coverage error
Not available.
13.3.1.4. Misclassification error
No13.3.1.4.1. Actions to minimise the misclassification error
Not applicable.
13.3.1.5. Contact error
No13.3.1.5.1. Actions to minimise the contact error
Contact information is constantly updated based on information from the Statistical Business Register, IACS, or directly from IFS questionnaires.
13.3.1.6. Impact of coverage error on data quality
Low13.3.2. Measurement error
See sub-categories below.
13.3.2.1. List of variables mostly affected by measurement errors
Not available.
13.3.2.2. Causes of measurement errors
Complexity of variablesRespondents’ inability to provide accurate answers
13.3.2.3. Actions to minimise the measurement error
Pre-testing questionnaireExplanatory notes or handbooks for enumerators or respondents
Training of enumerators
13.3.2.4. Impact of measurement error on data quality
Low13.3.2.5. Additional information measurement error
To minimise errors of measurement, data collection questionnaire was developed by chapters (General information, Land use, Livestock, etc.) and some implemented measures were helpful in this context:
- The reference moment or period was specified on every chapter heading;
- The questionnaire included the arithmetical checks between rows;
- If the queries had to be ticked off, mention was made on the questionnaire if it was a single or multiple answering variant.
Enumerators were trained to understand and respect some obligations that contributed to the reduction of measurement errors, as:
- The obligation to decline the official quality as enumerator by showing the personal identification card when first visiting an agricultural holding;
- Interviewing the most competent person from the agricultural holding, preferably the head of the agricultural holding;
- Avoiding the interview in front of people that do not belong to the concerned holding by explaining the information is confidential and to be used only for statistical purposes;
- To get precise and sincere replies, the questions were formulated clearly and politely;
- If the questions had several answer options, the interviewee was presented with a full list of them so they could choose the correct one;
- Taking down the replies as they were provided by the interviewee;
- Coming back to certain questions where the answer did not meet the arithmetical checks or if they did not correlate.
Due to the above measures, no major measurement errors were scored.
In addition, the data collection was done exclusively electronically and monitored using Survey Solutions software (CAPI method). Data collection using the CAPI method has as main advantage the assurance of a good quality of the collected data by implementing some sets of correlations and validations at the level of the questionnaire, active in real time (during the data collection).
13.3.3. Non response error
See sub-categories below.
13.3.3.1. Unit non-response - rate
See item 13.3.1.1.
The unit non-response rate is unweighted.
The unit non-response rate is calculated as the share of eligible non-respondent holdings to the eligible holdings. The eligible holdings include those holdings with unknown eligibility status which are imputed or re-weighted for (therefore considered eligible).
The unit non-response rate is calculated over the holdings in the main frame and frame extension, for which core data are sent to Eurostat.
Unit non-response rate is 2.1%.
13.3.3.1.1. Reasons for unit non-response
Refusal to participate13.3.3.1.2. Actions to minimise or address unit non-response
RemindersLegal actions
Weighting
13.3.3.1.3. Unit non-response analysis
The updated agricultural list of holdings was pre-loaded into electronic questionnaires (developed with World Bank Survey Solutions software).
Non-responses were monitored during the collection period through the facilities provided by the Survey Solutions software. The unresolved ones were treated during the processing period through reweighting.
The reason for the non-response at unit level was refusal to participate in interview. In such case, a re-weighting to adjust the grossing-up coefficients and also imputations were made for the eligible non-respondent units only. In Romania the non-response rate is very low, which makes the non-response bias to be not significant.
13.3.3.2. Item non-response - rate
No item non-response is registered at the end of the data collection process.
13.3.3.2.1. Variables with the highest item non-response rate
Not applicable.
13.3.3.2.2. Reasons for item non-response
Not applicable13.3.3.2.3. Actions to minimise or address item non-response
Follow-up interviewsReminders
Legal actions
13.3.3.3. Impact of non-response error on data quality
Low13.3.3.4. Additional information non-response error
Survey Solutions, as a CAPI method, improves quality of collected data through a series of built-in checks. This method has enabled us to validate data in real time because the platform’s programming allows automated skip patterns, displays error messages whenever unexpected values are entered by the interviewers, and follows other validation rules.
Interviewers (enumerators) see the following in the questionnaire: 1. Question to be answered. 2. Question that is not to be answered (skipped due to questionnaire logic). 3. Question that has been answered incorrectly (with instructions and an error message).
Once the interviewers have left for fieldwork, Survey Solutions has quality control functions that can be used by field supervisors and office-based staff (headquarters). Quality control performed by field supervisors is case-by-case checking, which is designed to mimic (electronically) the process of manual checking during data collection. In this approach, interviewers complete a questionnaire, then pass the form to their supervisor for review. After checking for mistakes, the supervisor returns the form to the interviewer for necessary corrections. In the case the application identifies unanswered questions, and the supervisor is required to reject the questionnaire, requiring the interviewer to answer all questions. Supervisors and headquarters cannot approve questionnaires containing unanswered questions.
At the questionnaire level, there is the following colour code available for all Survey Solutions roles: red for sections with errors, green for complete sections and blue for incomplete sections. Each questionnaire also shows the number of answered, unanswered and erroneous questions.
13.3.4. Processing error
See sub-categories below.
13.3.4.1. Sources of processing errors
Data processing13.3.4.2. Imputation methods
Deductive imputationMean imputation
Ratio imputation
13.3.4.3. Actions to correct or minimise processing errors
To minimise processing errors, the information system is extensively tested, and manual actions are minimised as much as possible. All corrections are made using scripts, eliminating manual adjustments. Before data is released, extensive checks and analyses are performed.
13.3.4.4. Tools and staff authorised to make corrections
Corrections of data were made as follows:
- via the logical controls included in the electronic questionnaires - corrections made by interviewers (enumerators) based on answers of respondents.
- analysis of extreme values during data collection as well as during data processing - corrections made by statisticians and IT teams in central and territorial statistical offices.
After the errors were analysed by NIS methodological team, the IT specialists from NIS made automatic corrections using Visual Fox Pro and R software tools.
13.3.4.5. Impact of processing error on data quality
Low13.3.4.6. Additional information processing error
Not available.
13.3.5. Model assumption error
Not applicable.
14.1. Timeliness
See sub-categories below.
14.1.1. Time lag - first result
Not applicable, because there are no preliminary results.
14.1.2. Time lag - final result
12 months.
14.2. Punctuality
See sub-categories below.
14.2.1. Punctuality - delivery and publication
See sub-categories below.
14.2.1.1. Punctuality - delivery
Not requested.
14.2.1.2. Punctuality - publication
The actual publication date coincides with the target date for data publication.
15.1. Comparability - geographical
See sub-categories below.
15.1.1. Asymmetry for mirror flow statistics - coefficient
Not applicable, because there are no mirror flows in Integrated Farm Statistics.
15.1.2. Definition of agricultural holding
See sub-categories below.
15.1.2.1. Deviations from Regulation (EU) 2018/1091
The definition of agricultural holdings is in accordance with Regulation (EU) 2018/1091.
15.1.2.2. Reasons for deviations
Not applicable.
15.1.3. Thresholds of agricultural holdings
See sub-categories below.
15.1.3.1. Proofs that the EU coverage requirements are met
IFS 2023 data collection was conducted as a sample survey. All variables for the core and modules were collected from a sample representing all agricultural holdings.
15.1.3.2. Differences between the national thresholds and the thresholds used for the data sent to Eurostat
No differences.
15.1.3.3. Reasons for differences
Not applicable.
15.1.4. Definitions and classifications of variables
See sub-categories below.
15.1.4.1. Deviations from Regulation (EU) 2018/1091 and EU handbook
There are no deviations in definitions and classifications of variables.
15.1.4.1.1. The number of working hours and days in a year corresponding to a full-time job
The information is available on Eurostat’s website, at the Circabc website.
The number of working hours and days in a year for a full-time job correspond to one annual working unit (AWU) in the country. One annual work unit corresponds to the work performed by one person who is occupied on an agricultural holding on a full-time basis. Annual working units are used to calculate the farm work on the agricultural holdings. (One annual working unit corresponds to 225 working days of eight hours each)
15.1.4.1.2. Point chosen in the Annual work unit (AWU) percentage band to calculate the AWU of holders, managers, family and non-family regular workers
See item 15.1.4.1.1.
15.1.4.1.3. AWU for workers of certain age groups
See item 15.1.4.1.1.
15.1.4.1.4. Livestock coefficients
No use of different LSU coefficients.
15.1.4.1.5. Livestock included in “Other livestock n.e.c.”
No deviations.
15.1.4.2. Reasons for deviations
Not applicable.
15.1.5. Reference periods/days
See sub-categories below.
15.1.5.1. Deviations from Regulation (EU) 2018/1091
No deviations.
15.1.5.2. Reasons for deviations
Not applicable.
15.1.6. Common land
The concept of common land exists15.1.6.1. Collection of common land data
Yes15.1.6.2. Reasons if common land exists and data are not collected
Not applicable.
15.1.6.3. Methods to record data on common land
Common land is included in the land of agricultural holdings renting or being allotted the land based on written or oral agreements.15.1.6.4. Source of collected data on common land
Surveys15.1.6.5. Description of methods to record data on common land
For these types of units there were used the same questionnaire as for the "regular" agricultural holdings.
15.1.6.6. Possible problems in relation to the collection of data on common land and proposals for future data collections
We do not experience problems to collect data on common land.
15.1.7. National standards and rules for certification of organic products
See sub-categories below.
15.1.7.1. Deviations from Council Regulation (EC) No 834/2007
No deviations.
15.1.7.2. Reasons for deviations
Not applicable.
15.1.8. Differences in methods across regions within the country
No differences.
15.2. Comparability - over time
See sub-categories below.
15.2.1. Length of comparable time series
NIS has published the data for the following 8 reference years: 2003, 2005, 2007, 2010, 2013, 2016, 2020 and 2023.
15.2.2. Definition of agricultural holding
See sub-categories below.
15.2.2.1. Changes since the last data transmission to Eurostat
There have been no changes15.2.2.2. Description of changes
There are no changes as both 2020 and 2023 are data collection years covered by the same Regulation (EU) 2018/1091.
15.2.3. Thresholds of agricultural holdings
See sub-categories below.
15.2.3.1. Changes in the thresholds of holdings for which core data are sent to Eurostat since the last data transmission
There have been no changes15.2.3.2. Description of changes
Not applicable.
15.2.4. Geographical coverage
See sub-categories below.
15.2.4.1. Change in the geographical coverage since the last data transmission to Eurostat
There have been no changes15.2.4.2. Description of changes
Not applicable.
15.2.5. Definitions and classifications of variables
See sub-categories below.
15.2.5.1. Changes since the last data transmission to Eurostat
There have been no changes15.2.5.2. Description of changes
There are no changes as both 2020 and 2023 are data collection years covered by the same Regulation (EU) 2018/1091.
15.2.6. Reference periods/days
See sub-categories below.
15.2.6.1. Changes since the last data transmission to Eurostat
There have been no changes15.2.6.2. Description of changes
Not applicable.
15.2.7. Common land
See sub-categories below.
15.2.7.1. Changes in the methods to record common land since the last data transmission to Eurostat
There have been some changes but not enough to warrant the designation of a break in series15.2.7.2. Description of changes
In 2020, both following methods are used:
- Common land was included in the land of entities meeting the definition of agricultural holdings, having own managers, and
- Common land was included in the land of agricultural holdings renting or being allotted the land based on written or oral agreements.
In 2023, only the following method is used: Common land was included in the land of agricultural holdings renting or being allotted the land based on written or oral agreements.
15.2.8. Explanations for major trends of main variables compared to the last data transmission to Eurostat
Compared to IFS 2020, in IFS 2023, the number of farms with UAA (excluding kitchen garden) fully converted or under conversion to organic farming increased as result of farmers who declared in 2023 very small areas cultivated organically but not registered with control and certification bodies.
Starting with statistics on agricultural input and output regulation, the plan (as recommended by the regulation) is to use data from control and certification bodies for a reliable count of farms and the organic area. We consider data from these bodies more reliable than that declared by farmers.
The number of farms having livestock also increased in 2023, compared to 2020. With regards to the holdings distribution by their SO_EURO class, there was an increase in the share of holdings falling in SO_EURO classes above 2 000 €, in the same time frame.
Compared to 2020, in 2023 they remarkably increased the number of holdings where the managers were involved in other gainful activities non related to the holdings.
15.2.9. Maintain of statistical identifiers over time
Yes15.3. Coherence - cross domain
See sub-categories below.
15.3.1. Coherence - sub annual and annual statistics
Not applicable to Integrated Farm Statistics, because there are no sub annual data collections in agriculture.
15.3.2. Coherence - National Accounts
Not applicable, because Integrated Farm Statistics have no relevance for national accounts.
15.3.3. Coherence at micro level with data collections in other domains in agriculture
See sub-categories below.
15.3.3.1. Analysis of coherence at micro level
Yes15.3.3.2. Results of analysis at micro level
Results are coherent at micro level for annual crop statistics and animal production statistics. There are no significant differences.
15.3.4. Coherence at macro level with data collections in other domains in agriculture
See sub-categories below.
15.3.4.1. Analysis of coherence at macro level
Yes15.3.4.2. Results of analysis at macro level
Coherence cross-domain: IFS vs CROP PRODUCTION
The main differences recorded were for F0000T, G0000T, J0000T, Q0000T, K0000T and V0000_S0000 and they are mainly small areas that have a small share in the total cultivated area.
Another cause of the differences between regions may be that the IFS recorded the entire area of the holding in one place, even if the holding owned land in several regions.
Coherence cross-domain: IFS vs ORGANIC CROP PRODUCTION
The data for 2023 come from the selective survey and those for 2022 from administrative sources. A possible cause of the differences marked for ARAT_ORG and PECRT_ORG is that these are crops that are grown on significant areas and are not reported separately in the IFS (for example sunflower) and farmers have omitted to report part of their area in the selective survey.
Coherence cross-domain: IFS vs ANIMAL PRODUCTION
Discrepancies were found for A2010, A2120, A2130, A2300G, A3110, A3120, A4120, A4220. The main reason for these discrepancies comes from the different reference moment of IFS 2023 and livestock survey.
The discrepancies recorded at the level of the development regions, come from the fact that, at the IFS 2020, the data was recorded according to the location of the agricultural holding. If an agricultural holding had the animals in several development regions, the livestock was recorded in a single place, where the agricultural holding is located, unlike the livestock survey, where the animal numbers were recorded at the level of each development region.
Coherence cross-domain: IFS vs ORGANIC ANIMAL PRODUCTION
Discrepancies were found only for A2300F_ORG, A4100_ORG and A4200_ORG and the main reason comes from the different reference moment of IFS 2023 and administrative sources. The data from IFS 2023 comes from sample-based statistical research compared to those from 2022 which come from administrative sources from control and certification bodies.
15.4. Coherence - internal
The data are internally consistent. This is ensured by the application of a wide range of validation rules.
See sub-categories below.
16.1. Coordination of data collections in agricultural statistics
There is no coordination of data collections.
16.2. Efficiency gains since the last data transmission to Eurostat
On-line surveysFurther automation
Further training
16.2.1. Additional information efficiency gains
Not available.
16.3. Average duration of farm interview (in minutes)
See sub-categories below.
16.3.1. Core
Not available.
16.3.2. Module ‘Labour force and other gainful activities‘
Not available.
16.3.3. Module ‘Rural development’
Not available.
16.3.4. Module ‘Animal housing and manure management’
Restricted from publication
16.3.5. Module ‘Irrigation’
Not available.
16.3.6. Module ‘Soil management practices’
Not available.
16.3.7. Module ‘Machinery and equipment’
Not available.
16.3.8. Module ‘Orchard’
Not available.
16.3.9. Module ‘Vineyard’
Restricted from publication
17.1. Data revision - policy
No revision policy.
17.2. Data revision - practice
No revisions were made.
17.2.1. Data revision - average size
Not requested.
18.1. Source data
See sub-categories below.
18.1.1. Sampling design & Procedure frame
See sub-categories below.
18.1.1.1. Type of frame
List frame18.1.1.2. Name of frame
Statistical Farm Register
18.1.1.3. Update frequency
Annual18.1.2. Core data collection on the main frame
See sub-categories below.
18.1.2.1. Coverage of agricultural holdings
Sample18.1.2.2. Sampling design
One-stage stratified random sampling. The stratification variable on unit location mentioned in concept 18.1.2.2.2 refers to NUTS 3 regions.
18.1.2.2.1. Name of sampling design
Stratified one-stage random sampling18.1.2.2.2. Stratification criteria
Unit sizeUnit location
Unit specialization
Unit legal status
18.1.2.2.3. Use of systematic sampling
No18.1.2.2.4. Full coverage strata
Full coverage strata for:
- Agricultural units with legal status.
- Holdings with mushrooms, hops, tobacco, rice and sugar beet.
18.1.2.2.5. Method of determination of the overall sample size
The overall sample size was determined by statistical and financial reasons.
18.1.2.2.6. Method of allocation of the overall sample size
Neymann allocation18.1.3. Core data collection on the frame extension
See sub-categories below.
18.1.3.1. Coverage of agricultural holdings
Sample18.1.3.2. Sampling design
One-stage stratified random sampling. The stratification variable on unit location mentioned in concept 18.1.3.2.2 refers to NUTS 3 regions.
18.1.3.2.1. Name of sampling design
Stratified one-stage random sampling18.1.3.2.2. Stratification criteria
Unit sizeUnit location
Unit specialization
Unit legal status
18.1.3.2.3. Use of systematic sampling
No18.1.3.2.4. Full coverage strata
Full coverage strata for:
- Agricultural units with legal status.
- Holdings with mushrooms, hops, tobacco, rice and sugar beet.
18.1.3.2.5. Method of determination of the overall sample size
The overall sample size was determined by statistical and financial reasons.
18.1.3.2.6. Method of allocation of the overall sample size
Neymann allocation18.1.4. Module “Labour force and other gainful activities”
See sub-categories below.
18.1.4.1. Coverage of agricultural holdings
Sample18.1.4.2. Sampling design
One-stage stratified random sampling. The stratification variable on unit location mentioned in concept 18.1.4.2.2 refers to NUTS 3 regions.
18.1.4.2.1. Name of sampling design
Stratified one-stage random sampling18.1.4.2.2. Stratification criteria
Unit sizeUnit location
Unit specialization
Unit legal status
18.1.4.2.3. Use of systematic sampling
No18.1.4.2.4. Full coverage strata
Full coverage strata for:
- Agricultural units with legal status.
- Holdings with mushrooms, hops, tobacco, rice and sugar beet.
18.1.4.2.5. Method of determination of the overall sample size
The overall sample size was determined by statistical and financial reasons.
18.1.4.2.6. Method of allocation of the overall sample size
Neymann allocation18.1.4.2.7. If sampled from the core sample, the sampling and calibration strategy
Not applicable18.1.5. Module “Rural development”
See sub-categories below.
18.1.5.1. Coverage of agricultural holdings
Sample18.1.5.2. Sampling design
One-stage stratified random sampling. The stratification variable on unit location mentioned in concept 18.1.5.2.2 refers to NUTS 3 regions.
18.1.5.2.1. Name of sampling design
Stratified one-stage random sampling18.1.5.2.2. Stratification criteria
Unit sizeUnit location
Unit specialization
Unit legal status
18.1.5.2.3. Use of systematic sampling
No18.1.5.2.4. Full coverage strata
Full coverage strata for:
- Agricultural units with legal status.
- Holdings with mushrooms, hops, tobacco, rice and sugar beet.
18.1.5.2.5. Method of determination of the overall sample size
The overall sample size was determined by statistical and financial reasons.
18.1.5.2.6. Method of allocation of the overall sample size
Neymann allocation18.1.5.2.7. If sampled from the core sample, the sampling strategy and calibration strategy
Not applicable18.1.6. Module “Animal housing and manure management module”
Restricted from publication
18.1.6.1. Coverage of agricultural holdings
Restricted from publication
18.1.6.2. Sampling design
Restricted from publication
18.1.6.2.1. Name of sampling design
Restricted from publication
18.1.6.2.2. Stratification criteria
Restricted from publication
18.1.6.2.3. Use of systematic sampling
Restricted from publication
18.1.6.2.4. Full coverage strata
Restricted from publication
18.1.6.2.5. Method of determination of the overall sample size
Restricted from publication
18.1.6.2.6. Method of allocation of the overall sample size
Restricted from publication
18.1.6.2.7. If sampled from the core sample, the sampling strategy and calibration strategy
Restricted from publication
18.1.7. Module ‘Irrigation’
See sub-categories below.
18.1.7.1. Coverage of agricultural holdings
Sample18.1.7.2. Sampling design
One-stage stratified random sampling. The stratification variable on unit location mentioned in concept 18.1.7.2.2 refers to NUTS 3 regions.
18.1.7.2.1. Name of sampling design
Stratified one-stage random sampling18.1.7.2.2. Stratification criteria
Unit sizeUnit location
Unit specialization
Unit legal status
18.1.7.2.3. Use of systematic sampling
No18.1.7.2.4. Full coverage strata
Full coverage strata for:
- Agricultural units with legal status with irrigable area.
- Holdings with mushrooms, hops, tobacco, rice and sugar beet.
18.1.7.2.5. Method of determination of the overall sample size
The overall sample size was determined by statistical and financial reasons.
18.1.7.2.6. Method of allocation of the overall sample size
Neymann allocation18.1.7.2.7. If sampled from the core sample, the sampling strategy and calibration strategy
Not applicable18.1.8. Module ‘Soil management practices’
See sub-categories below.
18.1.8.1. Coverage of agricultural holdings
Sample18.1.8.2. Sampling design
One-stage stratified random sampling. The stratification variable on unit location mentioned in concept 18.1.8.2.2 refers to NUTS 3 regions.
18.1.8.2.1. Name of sampling design
Stratified one-stage random sampling18.1.8.2.2. Stratification criteria
Unit sizeUnit location
Unit specialization
Unit legal status
18.1.8.2.3. Use of systematic sampling
No18.1.8.2.4. Full coverage strata
Full coverage strata for:
- Agricultural units with legal status.
- Holdings with hops, tobacco, rice and sugar beet.
18.1.8.2.5. Method of determination of the overall sample size
The overall sample size was determined by statistical and financial reasons.
18.1.8.2.6. Method of allocation of the overall sample size
Neymann allocation18.1.8.2.7. If sampled from the core sample, the sampling strategy and calibration strategy
Not applicable18.1.9. Module ‘Machinery and equipment’
See sub-categories below.
18.1.9.1. Coverage of agricultural holdings
Sample18.1.9.2. Sampling design
One-stage stratified random sampling. The stratification variable on unit location mentioned in concept 18.1.9.2.2 refers to NUTS 3 regions.
18.1.9.2.1. Name of sampling design
Stratified one-stage random sampling18.1.9.2.2. Stratification criteria
Unit sizeUnit location
Unit specialization
Unit legal status
18.1.9.2.3. Use of systematic sampling
No18.1.9.2.4. Full coverage strata
Full coverage strata for:
- Agricultural units with legal status.
- Holdings with mushrooms, hops, tobacco, rice and sugar beet.
18.1.9.2.5. Method of determination of the overall sample size
The overall sample size was determined by statistical and financial reasons.
18.1.9.2.6. Method of allocation of the overall sample size
Neymann allocation18.1.9.2.7. If sampled from the core sample, the sampling strategy and calibration strategy
Not applicable18.1.10. Module ‘Orchard’
See sub-categories below.
18.1.10.1. Coverage of agricultural holdings
Sample18.1.10.2. Sampling design
One-stage stratified random sampling. The stratification variable on unit location mentioned in concept 18.1.10.2.2 refers to NUTS 3 regions.
18.1.10.2.1. Name of sampling design
Stratified one-stage random sampling18.1.10.2.2. Stratification criteria
Unit sizeUnit location
Unit specialization
Unit legal status
18.1.10.2.3. Use of systematic sampling
No18.1.10.2.4. Full coverage strata
Full coverage strata for agricultural units with legal status.
18.1.10.2.5. Method of determination of the overall sample size
The overall sample size was determined by statistical and financial reasons.
18.1.10.2.6. Method of allocation of the overall sample size
Neymann allocation18.1.10.2.7. If sampled from the core sample, the sampling strategy and calibration strategy
Not applicable18.1.11. Module ‘Vineyard’
Restricted from publication
18.1.11.1. Coverage of agricultural holdings
Restricted from publication
18.1.11.2. Sampling design
Restricted from publication
18.1.11.2.1. Name of sampling design
Restricted from publication
18.1.11.2.2. Stratification criteria
Restricted from publication
18.1.11.2.3. Use of systematic sampling
Restricted from publication
18.1.11.2.4. Full coverage strata
Restricted from publication
18.1.11.2.5. Method of determination of the overall sample size
Restricted from publication
18.1.11.2.6. Method of allocation of the overall sample size
Restricted from publication
18.1.11.2.7. If sampled from the core sample, the sampling strategy and calibration strategy
Restricted from publication
18.1.12. Software tool used for sample selection
SAS
18.1.13. Administrative sources
See sub-categories below.
18.1.13.1. Administrative sources used and the purposes of using them
The information is available on Eurostat’s website, at the link: Additional data - Eurostat (europa.eu).
The information at the above link indicates that no administrative sources are used.
18.1.13.2. Description and quality of the administrative sources
No administrative sources are used.
18.1.13.3. Difficulties using additional administrative sources not currently used
Problems related to data quality of the sourceRisk concerning the stability of the source to political changes
18.1.14. Innovative approaches
The information on the innovative approaches and the quality methods applied is available on Eurostat’s website, at the link: Additional data - Eurostat (europa.eu).
The information at the above link indicates that no innovative approaches are used.
18.2. Frequency of data collection
Every 10 years for censuses and every 3-4 years between census years for all other IFS/FSS data collections.
18.3. Data collection
See sub-categories below.
18.3.1. Methods of data collection
Face-to-face, electronic version18.3.2. Data entry method, if paper questionnaires
Not applicable18.3.3. Questionnaire
For IFS 2023, Romania used for data collection an electronic questionnaire developed in Survey Solutions application (CAPI questionnaire used for all units/respondents and all variables). Survey Solutions also collects massive amounts of auxiliary data (known as paradata) on the interview process, which allows the calculation of a large number of indicators to assess the quality of data collected both in real time and on the basis of data exports with a lower or higher periodicity, depending on the chosen analysis plan.
Annexes:
18.3.3. Questionnaire in Romanian
18.3.3. Questionnaire in English
18.4. Data validation
See sub-categories below.
18.4.1. Type of validation checks
Data format checksCompleteness checks
Routing checks
Range checks
Relational checks
Comparisons with previous rounds of the data collection
Comparisons with other domains in agricultural statistics
18.4.2. Staff involved in data validation
InterviewersSupervisors
Staff from local departments
Staff from central department
Other
18.4.3. Tools used for data validation
At the questionnaire level, validation condition is included for each variable in the electronic questionnaire (CAPI).
Validation between different chapters of the questionnaires is performed using a customised, in-house IT application.
Survey Solutions has four levels of quality control for ensuring quality of data: automatic validations, supervisor data verification, headquarter data verification, and optional external validation.
- Automatic rule-based validation helps notify the interviewers about the data problems immediately, still during the interview when they are easiest to fix.
- Supervisor validation allows benefiting from supervisors’ intuition and knowledge of the area of data collection, and helps in verifying the interviewers follow the data collection protocol.
- Headquarter validation allows headquarter users to centrally monitor the quality of the incoming data, adherence to the established procedures, identify the problems appearing in the field, reject questionnaires approved by supervisors, which still do not satisfy the requirements.
- Optional external validation allows exporting the data and utilising external tools (or external data sources) not available in Survey Solutions to validate the survey data at regular intervals (daily in case of Romania). This allows searching for errors across all the interviews, for example to identify outliers. Each of these layers of defence allows improvements in data quality. But they are most effective in their combination. In addition, the use of Survey Solutions simplifies navigation in the questionnaire, automatically hides questions to be skipped, and provides proper input controls corresponding to the question types further reducing the possibility for user mistakes and improving the quality of the data.
18.5. Data compilation
An adjustment of the grossing-up coefficients for each stratum was made to account for non-response by calculating a non-response adjustment factor. This factor was estimated as the ratio of the total number of units in the sample in the stratum to the number of responding units in that stratum.
18.5.1. Imputation - rate
The weighted imputation rate is 2.1%.
18.5.2. Methods used to derive the extrapolation factor
Design weightNon-response adjustment
Calibration
18.6. Adjustment
Covered under Data compilation.
18.6.1. Seasonal adjustment
Not applicable to Integrated Farm Statistics, because it collects structural data on agriculture.
See sub-categories below.
19.1. List of abbreviations
AWU – Annual working unit
CAP – Common Agricultural Policy
CAPI – Computer Assisted Personal Interview
EU – European Union
FSS – Farm Structure Survey
GDPR – General Data Protection Regulation
GSBPM – Generic Statistical Business Process Model
IACS – Integrated Administration and Control System
IFS – Integrated Farm Statistics
LSU – Livestock unit
NIS – National Institute of Statistics
NUTS – Nomenclature of territorial units for statistics
RSE – Relative Standard Error
SGM – Standard Gross Margin
SO – Standard output
UAA – Utilised agricultural area
19.2. Additional comments
No additional comments.
The data describe the structure of agricultural holdings providing the general characteristics of farms and farmers and information on their land, livestock and labour force. They also describe production methods, rural development measures and agro-environmental aspects that look at the impact of agriculture on the environment.
The data are used by public, researchers, farmers and policy-makers to better understand the state of the farming sector and the impact of agriculture on the environment. The data follow up the changes in the agricultural sector and provide a basis for decision-making in the Common Agricultural Policy (CAP) and other European Union policies.
The statistical unit is the agricultural holding (farm). The aggregated results are disseminated through statistical tables. The data are presented at different geographical levels and over periods.
The data collections are organised in line with Regulation (EU) 2018/1091 and have a new structure, consisting of a core data set and several modules. The regulation covers the data collections in 2019/2020 (the agricultural census), 2023 and 2026. The data are as comparable and coherent as possible with the other European countries.
8 May 2025
The list of core variables is set in Annex III of Regulation (EU) 2018/1091.
The descriptions of the core variables as well as the lists and descriptions of the variables for the modules collected in 2023 are set in Commission Implementing Regulation (EU) 2021/2286.
The following groups of variables are collected in 2023:
- for core: location of the holding, legal personality of the holding, manager, type of tenure of the utilised agricultural area, variables of land, organic farming, irrigation on cultivated outdoor area, variables of livestock, organic production methods applied to animal production;
- for the module "Labour force and other gainful activities": farm management, family labour force, non-family labour force, other gainful activities directly and not directly related to the agricultural holding;
- for the module "Rural development": support received by agricultural holdings through various rural development measures;
- for the module “Irrigation”: availability of irrigation, irrigation methods, sources of irrigation water, technical parameters of the irrigation equipment, crops irrigated during a 12 months period;
- for the module “Soil management practices”: tillage methods, soil cover on arable land, crop rotation on arable land, ecological focus area;
- for the module “Machinery and equipment”: internet facilities, basic machinery, use of precision farming, machinery for livestock management, storage for agricultural products, equipment used for production of renewable energy on agricultural holdings;
- for the module “Orchards”: apples area, pears area, peaches area, nectarines area, apricots area, grapes for table use area, grapes for raisins area, each one by age of plantation and density of trees.
See sub-category below.
See sub-categories below.
See sub-categories below.
See sub-categories below.
See categories below.
Two kinds of units are generally used:
- the units of measurement for the variables (area in hectares, livestock in (1000) heads or LSU (livestock units), labour force in persons or AWU (annual working units), standard output in Euro, places for animal housing etc.) and
- the number of agricultural holdings having these characteristics.
An adjustment of the grossing-up coefficients for each stratum was made to account for non-response by calculating a non-response adjustment factor. This factor was estimated as the ratio of the total number of units in the sample in the stratum to the number of responding units in that stratum.
See sub-categories below.
Every 10 years for censuses and every 3-4 years between census years for all other IFS/FSS data collections.
See sub-categories below.
See sub-categories below.
See sub-categories below.
